Differences between Multi class and Multi label Classification
Features |
Multi class classification. |
Multi label classification |
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Output Structure: |
The output is a single class label assigned to each instance, indicating the most probable or correct class. |
The output is a set of binary values indicating the presence or absence of each label for each instance. Instances can be associated with multiple labels simultaneously. |
Model Output: |
the model assigns a single class label to each instance based on the class with the highest probability or confidence. |
The model outputs a binary vector for each instance, where each element corresponds to a label, indicating whether it is present or not. |
Training Techniques: |
Techniques like softmax activation and categorical cross-entropy loss are commonly used for training models to handle multiple classes. |
Techniques like sigmoid activation and binary cross-entropy loss are employed, treating each label independently. |
Class Assignment: |
Each instance is assigned to one and only one class, making the classification mutually exclusive. |
Instances can be associated with multiple labels, allowing for overlapping or shared characteristics. |
Evaluation Metrics: |
Metrics such as accuracy, precision, recall, and F1 score are commonly used to assess the overall performance of the model.
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Metrics like Hamming loss, precision at k, and recall at k are more appropriate, as they account for the presence of multiple labels for each instance. |
Model Complexity: |
Generally considered simpler as it involves assigning instances to exclusive classes. |
Can be more complex due to the need to capture dependencies and correlations between multiple labels. |
Problem Complexity: |
Typically used for simpler problems where instances belong to mutually exclusive categories. |
Suited for more complex scenarios where instances can exhibit characteristics of multiple labels simultaneously. |
Multiclass Classification vs Multi-label Classification
Multiclass classification is a machine learning task where the goal is to assign instances to one of multiple predefined classes or categories, where each instance belongs to exactly one class. Whereas multilabel classification is a machine learning task where each instance can be associated with multiple labels simultaneously, allowing for the assignment of multiple binary labels to the instance. In this article we are going to understand the multi-class classification and multi-label classification, how they are different, how they are evaluated, how to choose the best method for your problem, and much more.